AIM: Given a reference sequence and a set of short reads, align each read to the reference sequence finding the most likely origin of the read sequence.

• Traditional alignment perform base-by-base alignment
  
• It is (relatively) slow and computationally intensive

• Traditional alignment perform base-by-base alignment
  
• It is (relatively) slow and computationally intensive

• Traditional alignment perform base-by-base alignment
  
• It is (relatively) slow and computationally intensive

• These tools avoids base-to-base alignment of the reads

• ~ 20 times faster than the traditional alignment tools like STAR, HISAT2, etc.

• Unlike alignment based methods, pseudo-alignment methods focus on transcriptome
  (~2% of the genome in human)

• Use exact k-mer matching rather than aligning whole reads with mismatches and indels

• Alignment based

  • BAM file as input → reads must be mapped prior to quantification
  • Quantification using simple counting procedure
  • Pros: Intuitive
  • Cons: Slow and cannot correct for biases in RNA-seq data
  • Tools: HTseq, SubRead, etc.

• Alignment-free (quasi-mapping or pseudoalignment)

  • FASTQ files as input → base-to-base alignment of the reads is avoided
  • Pros: Very fast and considers biases in sequence composition
  • Cons: Not as intuitive
  • Tools: Kallisto, Sailfish, Salmon, etc.

Quantification: How many reads have come from a genomic feature?

• genomic feature can be a gene or transcript or exon, but usually gene

If we had mapped our reads to the genome (rather than the transcript sequences), our mapping would look like this:

As we know the transcript locations (from an annotation file in GFF or GTF format), the simplest approach is to count how many reads overlap each gene.

Salmon performs quantification as part of the quasi-mapping process, directly against the transcriptome.

Algorithm accounts for several biases:

• Multimapping: reads which map equally well to multiple locations.
• GC bias: higher GC content sequences are less likely to be observed as PCR is not as efficient.
• Positional bias: the 3’ end of transcripts are often more likely to be observed.
• Complexity bias: some sequences are easier to be bound and amplified than others.
• Sequence-based bias: differential binding efficiency of random hexamer primers during PCR.
• Fragment length bias: induced by size selection.

Genome/Transcriptome indexing is often required by bioinformatic tools such as aligners.

Similar to a book index, it allows the (pseudo)alignment algorithms to find regions of the large reference genome much quicker.

Salmon:

• Index is built for the transcriptome reference

• But we also include the reference genome as a “decoy”

  • Accounts for mapping of reads that might be similar to an annotated transcript, but are in fact from residual DNA contamination.

Example code:

# concatenate FASTA files for the transcriptome and genome
cat transcripts.fasta genome.fasta > gentrome.fasta

# create a text file with the names of the genomic sequences (i.e. chrosomomes/scaffolds)
zcat genome.fasta | grep ">" | cut -d " " -f 1 | sed 's/>//' > decoys.txt

# run the indexing
salmon index -t references/gentrome.fasta -d decoys.txt -i salmon_index

Once we have the index, we can proceed with quasi-mapping and quantification:

salmon quant \
    -i salmon_index \
    -l A \
    -1 SAMPLE_R1.fastq.gz \
    -2 SAMPLE_R2.fastq.gz \
    -o output/directory/SAMPLE \
    --gcBias --seqBias

Using --gcBias and --seqBias options accounts for the biases mentioned earlier

Main output is a tab-delimited (TSV) file for each sample we process:

Name                  Length  EffectiveLength  TPM       NumReads
ENSMUST00000177564.1  16      15.000           0.000000  0.000
ENSMUST00000196221.1  9       9.000            0.000000  0.000
ENSMUST00000179664.1  11      11.000           0.000000  0.000
ENSMUST00000178537.1  12      12.000           0.000000  0.000
ENSMUST00000178862.1  14      13.000           0.000000  0.000
ENSMUST00000179520.1  11      11.000           0.000000  0.000
ENSMUST00000179883.1  16      15.000           0.000000  0.000
ENSMUST00000195858.1  10      10.000           0.000000  0.000
ENSMUST00000179932.1  12      12.000           0.000000  0.000

1. Create and index to the transcriptome with Salmon
2. Quantify transcript expression using Salmon